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Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

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Abstract

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 \( ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) \). Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.

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References

  • Akgun A, Turk N (2010) Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multi-criteria decision analysis. Environ Earth Sci 61:595–611

    Article  Google Scholar 

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34

    Article  Google Scholar 

  • Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58:21–44

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) Generalized linear modelling of susceptibility to landsliding in the central Appennines, Italy. Comput Geosci 24(4):373–385

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31

    Article  Google Scholar 

  • Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81:432–445

    Article  Google Scholar 

  • Bai S, Wang J, Lu G, Zhou P, Hou S, Xu S (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31

    Article  Google Scholar 

  • Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Math Geosci 44:47–70

    Article  Google Scholar 

  • Barredo JI, Benavidesz A, Herh J, Van Westen CJ (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J Appl Earth Obs 2:9–23

    Article  Google Scholar 

  • Bednarik M, Magulova B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kralovany–Liptovsky Mikulas railway case study. Phys Chem Earth Parts A/B/C 35(3–5):162–171

    Article  Google Scholar 

  • Bijukchhen SM, Kayastha P, Dhital MR (2012) A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, east Nepal. Arab J Geosci. doi:10.1007/s12517-012-0569-7

    Google Scholar 

  • Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. Geographical information systems in assessing natural hazards. Kluwer Academic Publishers, Dordrecht, pp 135–175

    Google Scholar 

  • Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44:949–962

    Article  Google Scholar 

  • Chen Y, Yu J, Shahbaz K, Xevi E (2009) A GIS-based sensitivity analysis of multi-criteria weights. In: Proceedings of the 18th World IMACS/MODSIM Congress, Cairns, Australia 13–17 July 2009. http://mssanz.org.au/modsim09

  • Constantin M, Bednarik M, Jurchescu MC, Vlaicu M (2011) Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania). Environ Earth Sci 63:397–406

    Article  Google Scholar 

  • CRED (2009) Centre for Research on the Epidemiology of Disasters (CRED) website. http://www.dmdat.be/

  • Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Takuro M, Nishino K (2008) GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol 54:311–324

    Article  Google Scholar 

  • Demir G, Aytekin M, Akgun A, Ikizler SB, Tatar O (2012) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards. doi:10.1007/s11069-012-0418-8

    Google Scholar 

  • Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards 65:135–165

    Article  Google Scholar 

  • ECInc (Expert Choice Inc.) (1995) Decision support software: tutorial, expert choice, Student Version 9. Expert Choice Inc., Pittsburgh

    Google Scholar 

  • Egan JP (1975) Signal detection theory and ROC analysis. NY: Acad 195:266–268

  • Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Eng Geol 75:229–250

    Article  Google Scholar 

  • Ercanoğlu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949–964

    Article  Google Scholar 

  • Erner A, Duzgun HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Environ Earth Sci 66:859–877

    Article  Google Scholar 

  • Erner A, Sebnem H, Duzgun B (2010) Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7:55–68

    Article  Google Scholar 

  • Esmali Ouri A, Amirian S (2009) Landslide hazard zonation using MR and AHP methods and GIS techniques in Langan watershed, Ardabil, Iran. International Conference on ACRS 2009, Beijing, China

  • Falaschi F, Giacomelli F, Federici PR, Puccinelli A, D’Amato Avanzi G, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50:551–569

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2012a) Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS. J Environ Plan Manag. doi:10.1080/09640568.2011.646964

    Google Scholar 

  • Feizizadeh B, Blaschke T (2012b) GIS-Multi-criteria Decision Analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards. doi:10.1007/s11069-012-0463-3

    Google Scholar 

  • Fotheringham AS, Charlton ME, Brunsdon C (2001) Spatial variations in school performance: a local analysis using geographically weighted regression. Geogr Environ Model 5:43–66

    Article  Google Scholar 

  • Garcia-Rodriguez MJ, Malpica JA, Benito B, Diaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95:172–191

    Article  Google Scholar 

  • Geology Survey of Iran (GSI) (1997) http://www.gsi.ir/Main/Lang_en/index.html

  • Ghosh S (2011) Knowledge guided empirical prediction of landslide hazard, a dissertation to obtain the degree of doctor at the University of Twente, p 214

  • Guzzetti F, Carrarra A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:81–216

    Article  Google Scholar 

  • Guzzetti F, Cardinali M, Reichenbach P, Carrara A (2000) Comparing landslide maps: a case study in the upper Tiber River Basin, central Italy. Environ Manag 25(3):247–363

    Article  Google Scholar 

  • Guzzetti F (2005) Landslide hazard and risk assessment. PhD Dissertation, Rheinischen Friedrich-Wilhelms-University Bonn, 389pp

  • Hall FG, Townshend JR, Engman ET (1995) Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sens Environ 51:138–156. doi:10.1016/0034-4257

    Article  Google Scholar 

  • Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2):1157–1179

    Article  Google Scholar 

  • Hengl T, Gruber S, Shrestha DP (2003) Digital terrain analysis in ILWIS. International Institute for Geo-Information Science and Earth Observation Enschede, The Netherlands, p 62

    Google Scholar 

  • Jankowski P (1995) Integrating geographical information systems and multiple criteria decision-making methods. Int J Geogr Inf Sci 9:251–273

    Article  Google Scholar 

  • Jin GC, Che OhY, Choi CU (2010) The comparative research of landslide susceptibility mapping using FR, AHP, LR, ANN. Korean Soc Geosp Inf Syst 9:13–20

    Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2013) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. doi:10.1007/s10346-013-0391-7

    Google Scholar 

  • Kevin LKW, Tay LT, Lateh H (2011) Landslide hazard mapping of Penang Island using probabilistic methods and logistic regression. Imaging Systems and Techniques (IST), 2011 IEEE International Conference, pp 273–278

  • Kheirkhah Zarkesh MM (2005) Decision support system for floodwater spreading site selection in Iran. Thesis to fulfil the requirements for the degree of Doctor on the authority of the rector magnificus of Wageningen University, p 259

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74:17–28

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26:1477–1491

    Article  Google Scholar 

  • Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71:289–302

    Article  Google Scholar 

  • Li C, Ma T, Sun L, Li W, Zheng A (2011) Application and verification of fractal approach to landslide susceptibility mapping. Natl Hazards. doi:10.1007/s11069-011-9804-x

    Google Scholar 

  • Majtan S, Omura H, Morita K (2002) Fractal dimension as an indicator of probability for landslides in North Matsuura, Japan. Geografický Casopis 54(1):5–19

    Google Scholar 

  • Malczewski J (1999) GIS and multi-criteria decision analysis. Wiley, New York, p 392

    Google Scholar 

  • Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123:225–234

    Article  Google Scholar 

  • Miller DJ, Sias J (1998) Deciphering large landslides: linking hydrologic, groundwater, and slope-stability model through GIS. Hydro Process 12(6):924–942

    Article  Google Scholar 

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236

    Article  Google Scholar 

  • Moore ID, Burch GJ (1986) Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Res 22:1350–1360

    Article  Google Scholar 

  • Moore ID, Wilson JP (1992) Length-slope factors for the revised universal soil loss equation: simplified method of estimation. J Soil Water Conserv 47:423–428

    Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydro Process 5:3–30

    Article  Google Scholar 

  • Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20

    Article  Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97:171–191

    Article  Google Scholar 

  • Nefeslioglu HA, Sezer E, Gökçeoğlu C, Bozkır AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Math Probl in Eng, 2010, Article ID: 901095

  • Nie HF, Diao SJ, Liu JX, Huang H (2001) The application of remote sensing technique and AHP-fuzzy method in comprehensive analysis and assessment for regional stability of Chongqing City, China. In: Proceedings of the 22nd international Asian Conference on Remote Sensing, vol 1, pp 660–665

  • O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690

    Article  Google Scholar 

  • Oh HJ, Lee S (2010) Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Adv 3:44–55

    Google Scholar 

  • Oh HJ, Lee S (2011) Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environ Earth Sci 62:935–951

    Article  Google Scholar 

  • Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276. doi:10.1016/j.cageo.2010.10.012

    Article  Google Scholar 

  • Oh HJ, Lee S, Chotikasathien W, Kim CH, Kwon JH (2009) Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environ Geol 57:641–651

    Article  Google Scholar 

  • Ohlmacher CG, Davis CJ (2003) Using multiple regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69:331–343

    Article  Google Scholar 

  • Ozdemir A (2009) Landslide susceptibility mapping of vicinity of Yaka Landslide (Gelendost, Turkey) using conditional probability approach in GIS. Environ Geol 57:1675–1686

    Article  Google Scholar 

  • Ozdemir A (2011) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J Hydrol 405:123–136

    Article  Google Scholar 

  • Ozdemir A, Altural T (2012) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci. doi:10.1016/j.jseaes.2012.12.014

  • Park S, Choi C, Kim B, Kim J (2012) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci. doi:10.1007/s12665-012-1842-5

    Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012a) Application of weights-of evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. doi:10.1007/s12517-012-0532-7

    Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Deylami Moezzi K (2012b) A comparative assessment of prediction capabilities of Dempster-Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS. Geomat Natl Hazards Risk. doi:10.1080/19475705.2012.662915

    Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012c) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012d) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84

    Article  Google Scholar 

  • Pourghasemi HR, Gokceoglu C, Pradhan B, Deylami Moezzi K (2012e) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Pradhan B, Buchroithner M (eds) Terrigenous mass movements. Springer, Berlin, pp 23–49. doi:10.1007/978-3-642-25495-6-2

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012f) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s entropy and GIS, AEROTECH IV–2012, Kuala Lumpur, Malaysia. Appl Mech Mater 225:486–491. doi:10.4028/www.scientific.net/AMM.225.486

  • Pourghasemi HR, Goli Jirandeh A, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS. J Earth Syst Sci 122(2):349–369

    Google Scholar 

  • Pradhan B (2010a) Application of an advanced fuzzy logic model for landslide susceptibility analysis. Int J Comput Intell Syst 3(3):370–381

    Google Scholar 

  • Pradhan B (2010b) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320

    Article  Google Scholar 

  • Pradhan B (2011a) Manifestation of an advanced fuzzy logic model coupled with geo-information techniques for landslide susceptibility analysis. Environ Ecol Stat 18(3):471–493

    Article  Google Scholar 

  • Pradhan B (2011b) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63(2):329–349

    Article  Google Scholar 

  • Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

    Article  Google Scholar 

  • Pradhan B, Buchroithner MF (2010) Comparison and validation of landslide susceptibility maps using an artificial neural network model for three test areas in Malaysia. Environ Eng Geosci 16(2):107–126

    Article  Google Scholar 

  • Pradhan B, Lee S (2010a) Delineation of landslide hazard areas using frequency ratio, logistic regression and artificial neural network model at Penang Island, Malaysia. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  • Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3:319–326

    Article  Google Scholar 

  • Raman R, Punia M (2012) The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley, Western Himalaya, India. Georisk Assess Manag Risk Eng Syst Geohazards 6(3):145–161

    Article  Google Scholar 

  • Rautela P, Lakhera RC (2000) Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). Int J Appl Earth Obs Geoinf 2:153–160

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Pradhan B, Pourghasemi HR, Khumamoto T, Akgun A (2013) Application of frequency ratio, statistical index and weights-of-evidence models, and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. doi:10.1007/s12517-012-0807-z

    Google Scholar 

  • Rozos D, Pyrgiotis L, Skias S, Tsagaratos P (2008) An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory, an application in Karditsa County. Landslides 5:261–270

    Article  Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281

    Article  Google Scholar 

  • Saaty T (1980) The analytical hierarchy process. McGraw-Hill, New York

    Google Scholar 

  • Saaty TL (1994) Fundamentals of decision making and priority theory with analytic hierarchy process. RWS Publications, Pittsburgh, p 527

    Google Scholar 

  • Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98

    Google Scholar 

  • Schumacher M, Robner R, Vach W (1996) Neural networks and logistic regression: Part 1. Comput Stat Data Anal 21:661–682

    Article  Google Scholar 

  • Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  • Shahabi H, Ahmad BB, Khezri S (2012) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arab J Geosci. doi:10.1007/s12517-012-0650-2

    Google Scholar 

  • Sidle RC, Ochiai H (2006) Landslides: processes, prediction, and landuse. American Geophysical Union, Washington, D.C. Water Res Monograph 18, p 312

  • Soeters R, Van Westen CJ (1996) Slope instability recognition analysis and zonation. In: Turner KT, Schuster RL (eds) Landslide: investigation and mitigation. Spec Rep 47. Transportation Research Board, National Research Council, Washington, DC, pp 129–177

  • Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake-induced landslides using Bayesian network: a case study in Beichuan, China. Comput Geosci 42:189–199

    Article  Google Scholar 

  • Tien Bui D, Lofman O, Revhaug I, Dick O (2011a) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2011b) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro fuzzy inference system and GIS. Comput Geosci. doi:10.1016/j.cageo.2011.10.031

    Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree and Naïve Bayes models. Math Probl Eng 2012:1–26. doi:10.1155/2012/974638

    Article  Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2009) Landslide hazard zonation using quantitative methods in GIS. Int J Civil Eng 7(3):176–189

    Google Scholar 

  • Vahidnia MH, Alesheikh AA, Alimohammadi A, Hosseinali F (2010) A GIS based neuro fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping. Comput Geosci 36(9):1101–1114

    Article  Google Scholar 

  • van Westen C (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows application guide. ITC Publication, Enschede, pp 73–84

  • van Westen CJ, Rengers N, Terlien MTJ, Soeters R (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rundsch 86(2):404–414

    Article  Google Scholar 

  • van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  • van Westen CJ, Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation-why is it still so difficult? Bull Eng Geol Environ 65:67–184

    Google Scholar 

  • Vargas LG (1990) An overview of the analytic hierarchy process and its applications. Eur J Oper Res 48:2–8

    Article  Google Scholar 

  • Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RJ (eds) Landslides analysis and control. Special report, vol 176. Transportation Research Board, National Academy of Sciences, New York, pp 11–33

  • Varnes DJ (1984) With IAEG commission on landslides and other mass movements: landslide hazard zonations: a review of principles and practices. UNESCO, Paris, p 63

    Google Scholar 

  • Wan S (2012) Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping. Environ Earth Sci. doi:10.1007/s12665-012-1832-7

    Google Scholar 

  • Xu C, Xu X (2012) Controlling parameter analyses and hazard mapping for earthquake-triggered landslides: an example from a square region in Beichuan County, Sichuan Province, China. Arab J Geosci. doi:10.1007/s12517-012-0646-y

    Google Scholar 

  • Xu C, Xu X, Dai F, Xiao J (2012a) Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region. J Earth Sci 23(1):97–120

    Article  Google Scholar 

  • Xu C, Dai F, Xu X, Lee YH (2012b) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80

    Article  Google Scholar 

  • Xu C, Xu X, Lee YH, Tan X, Yu G, Dai F (2012c) The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling. Environ Earth Sci 66(6):1603–1616

    Article  Google Scholar 

  • Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12

    Article  Google Scholar 

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85:274–287

    Article  Google Scholar 

  • Yang Z, Lee YH (2006) The fractal characteristics of landslides induced by earthquakes and rainfall in central Taiwan, The Geological Society of London, pp 1–8

  • Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582

    Article  Google Scholar 

  • Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey, Ph.D Thesis. Department of Geomatics the University of Melbourne, p 423

  • Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Comput Geosci 35:1125–1138

    Google Scholar 

  • Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61:821–836

    Article  Google Scholar 

  • Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2012) Landslide susceptibility mapping at Vaz watershed (Iran) using an artificial neural network model: a comparison between multi-layer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci. doi:10.1007/s12517-012-0610-x

    Google Scholar 

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Acknowledgments

The authors gratefully acknowledge of National Geographic Organization (NGO) (http://www.ngo-iran.ir/ngo.htm) for providing the satellite images (IRS). This research was carried out as part of the first author’s PhD thesis at the watershed management engineering, Tarbiat Modares University (TMU), Mazandaran, Iran. Also, the authors would like to thank two anonymous reviewers and editor for their helpful comments on the previous version of the manuscript.

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Authors and Affiliations

  1. Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, Tarbiat Modares University (TMU), Noor, Mazandaran, Iran

    H. R. Pourghasemi & H. R. Moradi

  2. Department of Engineering Geology, Tarbiat Moallem University (Kharazmi University), Tehran, Iran

    S. M. Fatemi Aghda

  3. The Ministry of Road and Urban Development (Road, Housing and Urban Development Research Center), Tehran, Iran

    S. M. Fatemi Aghda

Authors
  1. H. R. Pourghasemi
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  2. H. R. Moradi
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  3. S. M. Fatemi Aghda
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Correspondence to H. R. Moradi.

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Pourghasemi, H.R., Moradi, H.R. & Fatemi Aghda, S.M. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69, 749–779 (2013). https://doi.org/10.1007/s11069-013-0728-5

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  • DOI: https://doi.org/10.1007/s11069-013-0728-5

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